Lozovina et al , 2009; Tan et al , 2009), in studies which develo

Lozovina et al., 2009; Tan et al., 2009), in studies which developed and validated sport-specific tests (Mujika et al., 2006; Platanou, 2005), investigations which Tubacin 537049-40-4 focused on the intensity of the game (V. Lozovina, et al., 2003), or sport tactics and related statistics of the water polo game (Platanou, 2004). However, most of the studies mentioned so far sampled adult athletes (e.g. senior-age water polo players), while position specifics were mostly analyzed among three or four playing positions (i.e. goalkeepers were frequently not included in the analysis, and/or drivers and wings were observed as a single group �C field players). As far as we are aware both problems are understandable. Water polo is not one of the most popular sports in the world (like football or basketball for example) and it is therefore hard to find an appropriate sample of subjects (i.

e. adequate number of adequately trained athletes). This is chiefly the case with goalkeepers (one or two in each team). The second problem (e.g. studies not sampling young athletes) is also a logical consequence of the available number of subjects. Most particularly, if the study of adolescent athletes is intended then, due to the process of biological maturation, the subjects have to be near the end of puberty and homogenous in age (one or two years�� age difference at the most) and/or biological age must be controlled in the analysis (Faigenbaum, et al., 2009; Gurd and Klentrou, 2003; Latt, et al., 2009; Nindl et al., 1995). Since diversity in age is not a factor which can influence anthropometric status and/or motor achievements in adulthood (i.

e. senior-age athletes), it is logically more convenient to study adult athletes. The overall status of athletes in most sports can be observed during general and specific fitness tests. While general fitness tests (i.e. general motor and/or endurance capacities) are important indices of overall fitness status and allow a comparison of athletes from different sports (Frenkl et al., 2001), specific fitness tests allow a more precise insight into sport-specific capacities and therefore provide a basis for comparing athletes in the same sport (Bampouras and Marrin, 2009; Holloway et al., 2008; Hughes et al., 2003; Sattler et al., 2011).

However, GSK-3 there is a clear lack of studies dealing with specific physical fitness profiles in water polo and, in particular, we found no study which has investigated this problem among high-quality junior water polo players. The aim of this study was to investigate the status and differences between five playing positions (Goalkeepers, Centers, Drivers, Wings and Points) in anthropometric measures and some specific physical fitness variables in high-level junior (17 to 18 years of age) water polo players. Material and Methods Participants The sample of subjects consisted of a total of 110 high-level water polo junior players.

, 2005) using different types of hand dynamometers Particularly,

, 2005) using different types of hand dynamometers. Particularly, Espana-Romero et al. (2008) reported high reliability (ICC = 0.97 �C 0.98) of the handgrip strength test in 6�C12 year-old children, using the Takey dynamometer. selleck kinase inhibitor Excellent test-retest reliability (r = 0.96 �C 0.98) of handgrip strength have been also showed in untrained adolescents (14�C17 years-old; Ruiz et al., 2006). In addition, Langerstrom et al. (1998) and Ruiz-Ruiz et al. (2002) found high reliability (r = 0.91 �C 0.97) of the handgrip strength test in healthy adults using the Grippit and Takei dynamometers, respectively. The results of this study are also, in accordance with those by Coelho e Silva et al. (2008; 2010) in young basketball players (14�C15.9 years-old and 12�C13.9 years-old, respectively) that reported high reliability (r = 0.

99) of handgrip strength using the Lafayette hand dynamometer. Table 3 Test-retest reliability of maximal handgrip strength in healthy children, adolescents and adults Our results support earlier findings that showed non-significant differences in handgrip strength between test and retest values (Espana-Romero et al., 2008; 2010a). In contrast, Clerke et al. (2005) found small but significant differences in handgrip strength between test and retest, in 13 to 17 year-old adolescents. The absence of warm-up or familiarization prior to testing in the above study may account for the differences in handgrip strength between test and retest measurements. Indeed, Svensson et al.

(2008), who also found differences in handgrip strength between test and retest suggested that children may learn over the trials a better technique or accomplish to squeeze harder. Therefore, the authors recommended a familiarization session and three maximal trials during the main testing. Reliability and age-effect Only a few studies addressed the issue of age-effect on reliability of handgrip strength in untrained participants (Table 4). The results of our study are in line with those of Espana-Romero et al. (2010a) who examined the reliability of the handgrip strength test in untrained children (6�C11 years-old) and adolescents (12�C18 years-old) using the Takey dynamometer and found high reliability in both age-groups. Moreover, Molenaar et al. (2008) compared the reliability of handgrip strength among three age-groups of untrained children (4�C6, 7�C9, and 10�C12 years old) using two different dynamometers (Lode dynamometer vs.

Martin vigorimeter), and reported no clear age-effect on reliability for both dynamometers. Brefeldin_A Table 4 Test-retest reliability of maximal handgrip strength at different age-group. In contrast, Svensson et al. (2008) compared the reliability of the handgrip strength test among 6, 10 and 14 year old untrained children using the Grippit dynamometer, and showed greater reliability in 6 and 14 year old (ICC = 0.96) compared to 10 year old children (ICC = 0.78).

The authors also wish to thank Rasit Yediveren for the valuable a

The authors also wish to thank Rasit Yediveren for the valuable assistance during the data collection stage.
Soccer is one of the most popular sports in the world, especially in Europe. Soccer is characterized by numerous short, explosive exercise bursts interspersed with brief recovery periods over an extended period of time (90 minutes) (Meckel et al., 2009). Soccer performance, selleck chem which depends on the technical skills and physical fitness of the players, is known to significantly influence match performance. The simultaneous use of both technical skills and fitness in soccer training would produce extremely effective performance (Little and Williams, 2007). Agility, acceleration, change of direction, deceleration, and sprinting are regarded as critical technical skills and the main components of soccer training.

The ability to sprint and to change direction while sprinting are determinants of performance in field sports, as evidenced by time and motion analysis (Sheppard and Young, 2006). In many sports, including soccer, athletes are required to accelerate, decelerate, and change direction throughout the game (Docherty et al., 1988). Often, these movements are performed in conjunction with passing, dribbling and striking movements (Abernethy and Russell, 1987; Farrow et al., 2005; Sheppard et al., 2006). Differences between higher and lower performers in anticipation and efficient decision making in accordance with sport-specific stimuli have also been mentioned in relevant literature (Abernethy and Russell, 1987; Tenenbaum et al., 1996; Farrow et al., 2005).

In soccer agility, anticipating the direction and timing of the ball are crucial issues for success (Sheppard et al., 2006). However, few studies have evaluated sport-specific, physical performance tests of agility, including sprints, changes of direction and striking at the goal. Therefore, the purpose of this study was to develop and evaluate a novel test of agility and striking skill for soccer that involves sprint running, direction changing, and kicking stationary balls to the goal with accurate decision making. The classical T-drill agility test, developed by Semenick (1990), was implemented with four balls and the goal (Figure 1). Figure 1 A diagram and explanation of the new developed agility and skill test for soccer.

Material and Methods Subjects A total of 113 amateur (38) and professional (32) male soccer players from the Turkish League (Kirikkale-wide from Division 3 and 1st Amateurs) (mean �� SD: age: 21.2 �� 3 years; body height: 1.78 �� 5.4 m; body mass: 72.2 �� 8.2 kg; body fat: 12.2 �� 3.9 %; years of experience: 6.8 �� 2.43) and university GSK-3 students (43) volunteered to participate in this study. The study protocol and methods were approved by the local institutional ethics committee of the University of Kirikkale, and all subjects gave written informed consent prior to participation.

013 m It was assumed that the maximal error of angle determinati

013 m. It was assumed that the maximal error of angle determination in this study was for a segment length of 0.55 m, at about 3.6 degrees. The precision limits for these angle measurements Perifosine structure resulted predominantly from the inexactness in determining the ankle, hip and shoulder reference points; an athlete in his suit is not a rigid body. Associated with this are angle measurement precision errors of typically 1�C2�� (Schm?lzer and M��ller, 2005). A six-link bilateral model was created (left ski, right ski, trunk, arm, thigh, shin) based on nine joint points (top of the skis, end of the skis, shoulder joint, distal arm joint, hip joint, knee joint and ankle joint) (Picture 2). Picture 2 The 2-D model of nine jumper��s body and skis points used in digitising The data were manually digitised by an experienced technician.

The changes of body and ski positions were mostly determined with respect to the horizontal plane. The set of eight kinematic variables was constructed (Figure 1). Figure 1 Set of kinematic variables at 15m behind the jumping hill edge; �� G- Angle between left skis and leg; ��T- Angle of hip extension; ��LR- Angle between upper body and left arm; ��N- Angle between left leg and horizontal axis; … Statistical analysis of all multi-item variables was performed to determine mean values (M) and standard deviations (SD). Pearson��s linear correlation coefficients (r) were computed. P-values of less than 0.05 were accepted as statistically significant. Factor component analysis was used to determine the common variance between the dependent multi-item variable length of jump and the chosen independent multi-item kinematic variables.

The following parameters were calculated: Fnp �C factors value of each manifest variable on extracted factors, F CUM �C cumulative factors value of each manifest variable of all extracted factors, % of TV �C percentage of total variance of all extracted factors. Results All correlation coefficients between the dependent multi-item variable length of the jump and the independent multi-item variable vertical height of flying (Table 1) were statistically significant (p<0.05). High factor projections of both multi-item variables vertical height of flying and length of jump existed in the first common factor, which explained 69.13 % of total variance. Statistically significantl (p<0.

05) coefficients of correlations between the multi-item variable angle between the body chord and horizontal axis and length of jump were reached. A high level GSK-3 of total variance (TV=65.04%) was seen in the first common factor. Also statistically significant correlation coefficients existed between the multi-item variable length of jump and the angle between the left leg and the horizontal axis. The variability of these coefficients was not high. The explained common variance (TV=61.88%) in the first factor was above 50 % of the total variance.

50 > BMI

50 > BMI http://www.selleckchem.com/products/PD-0332991.html > 24.99) according to WHO classification (WHO, 2004). Likewise, in case of weight/height indices, mean body fat percentage recorded in climbers was comparable to this observed in untrained students and amounted to 15.4%. However, when classified by Heath-Carter somatotype components, endomorphy component that reflects adiposity had the lowest contribution in climbers�� somatotype; the mean value being significantly (p<0.001) lower than that observed in untrained students (2.4 �� 0.79 vs. 3.6 �� 1.48, respectively). Regardless of comparable body height, climbers had significantly greater arm span and arm length (by about 6 and 2.5 cm, respectively) what was reflected in ape index and arm length index, the respective values being by about 1.5 (p<0.001) and 0.6 SD (p<0.

01) greater than observed in untrained students, respectively. Additionally, climbers exhibited significantly greater values in arm (32.7 �� 2.09 vs. 30.9 �� 2.52 cm) and forearm circumferences (28.3 �� 1.28 vs. 26.02 �� 1.80 cm) and in upper extremity girth index, while no differences were found for elbow width. On the other hand, climbers had by 1 SD (p<0.001) lesser knee width while no between-group differences were found for calf circumference. Moreover, climbers exhibited by about 1 SD less in pelvis-to-shoulder ratio comparing to untrained students. Likewise, for upper extremities climbers had significantly (p<0.05) longer lower limbs as expressed by the Manouvrier��s index. In order to reveal possible relationships between somatic indices and subjects�� climbing ability, Pearson��s correlation coefficients and partial correlations were calculated.

Apart from the obvious relations between the body fat and weight-to-height indices or between indices pertaining to the length of upper limb, significant negative correlations were found only for %FAT and ape index (?0.594; p<0,01) and for arm circumference index and BMI (r = ?0.497; p<0.05) or RI (r = ?0.587; p<0.01). Self-reported climbing ability significantly correlated with %FAT (r = ?0.614; p<0.01); besides that, no significant correlations with somatic indices were noted and none of the partial correlations proved significant. Only the ape index tended to correlate with the self-reported climbing ability (r = 0.397; p = 0.083). Discussion Despite the growing number of reports on rock climbing, those concerning anthropometric characteristics of climbers are rather scarce and inconsistent.

The results of this study do not support the view of Watts et al. (2003) that climbers are small in stature with low body mass as no differences between the climbers and untrained controls were found for basic Anacetrapib somatic features and body size-related indices. Body height and body mass of climbers were rather average and amounted to 180.0 cm and 70.7 kg, respectively, what was in line with the observations of Billat et al. (1995) and Grant et al.

However, they did not observe an improvement in VO2max after the

However, they did not observe an improvement in VO2max after the same training program performed in hypoxia thenthereby conditions (typical IHT) and normoxia. In addition, the results of our last study (Czuba et al., 2011) allow us to conclude that IHT for three weeks (3 IHT sessions per week) with prolonged exercise (30�C40min) at lactate threshold is an effective training means for improving VO2max and endurance performance at sea-level. This results are in accordance to those obtained by Dufour et al. (2006) and Zoll et al. (2006), where subjects trained with very similar intensity (at the second ventilatory threshold) during IHT sessions for six weeks, but twice a week. A comparison of the results of these experiments (Dufour et al., 2006; Zoll et al., 2006; Czuba et al.

, 2011) with those obtained by the authors suggests that exercising during IHT at the anaerobic threshold intensity selected individually to match the designated altitude (hypoxia) effectively improves aerobic capacity and exercise performance. Similarly, Robertson et al. (2010) observed a significant improvement in values of VO2max after 3 weeks of IHT protocol (4 training sessions per week at 2,200 m). The IHT sessions incorporated one long, one moderate duration, and two interval sessions with high intensity per week. The specific content of these sessions was based on individual training programmes with athletes instructed to complete between 4 and 5 h of hypoxic training per week, depending on their normal training load. Unfortunately, the authors did not report more details about the duration of IHT sessions.

There is also strong evidence that demonstrated no beneficial effects of IHT programs on aerobic capacity, when the intensity during IHT sessions was set below 80% of VO2max at sea level (Vallier et al., 1996; Truijens et al., 2003; Ventura et al., 2005). The absence of positive adaptive changes in these athletes is very probable due to insufficient exercise intensity during the IHT protocol. The recent study on IHT that was carried out with triathletes placed in a hypobaric chamber (Hendriksen and Meeuwsen, 2003) also failed to demonstrate improvements in VO2max. Exercise intensity in that study was selected individually to correspond to 60�C70% of Heart Rate Reserve (HRR), so the subjects exercised in the aerobic exercise zone during a 10-day training period.

During the annual training cycles, such training units are used to maintain the Cilengitide athlete��s fitness level and not to improve it. The results of this study build on and enhance the earlier research into the IHT method. In the analysed basketball players, significantly higher VO2max (by 8%) and longer distance covered in the maximal ramp test were recorded after 3 weeks of high intensity interval training in normobaric hypoxia. In the group H where the IHT protocol was applied, the increase in VO2max was greater by 5% than in the group C that trained in normoxia.